Computer and Modernization ›› 2024, Vol. 0 ›› Issue (10): 107-112.doi: 10.3969/j.issn.1006-2475.2024.10.017

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Label Recommendation Methods for Public Cultural Resources

  

  1. (1. School of Computer Science, Xi’an Polytechnic University, Xi’an 710600, China;
    2. The Shaanxi Key Laboratory of Clothing Intelligence, Xi’an 710600, China)
  • Online:2024-10-29 Published:2024-10-30

Abstract: Resource labels play an indispensable role in the era of information explosion, and the use of labels can greatly reduce the workload of recommendation systems and improve their accuracy. A public cultural resource recommendation method based on the relevance of resource labels is designed based on the resources of the national public cultural cloud platform. Firstly, the Integrating Global Semantics BERT TextCNN Model is proposed, which extracts the deep semantic relationships between local and global resource texts and labels to obtain the text correlation between resources and labels. Secondly, the keyword correlation between resources and labels is mined based on the TF-IDF algorithm. Finally, the correlation between resources and labels is obtained by using perceptron model and the recommended sequence of public cultural label resources is ultimately obtained. Multigroup experiments are conducted on the Reuters-21578 and National Public Culture Cloud datasets. The experiments results show that the resource recommendation effect of our method is superior to the baseline model.

Key words:  , label recommendation; multi-label classification; text features; public cultural resources

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